Acupuncture at pericardium 6 (PC6) has been clinically found to alleviate anxiety disorders and is thought to be related to regulatory effects on the autonomic nervous system, central nervous system, and default mode network. However, the relationship between the temporal characteristics of microstate changes and the related PC6 acupoints with twirling frequency manipulation remains unclear. In this study, resting-state electroencephalogram signals from 10 healthy participants were recorded at the PC6/sham acupoint with strong/weak twirling frequency manipulation. Electroencephalogram microstate analysis was conducted to obtain microstate maps and microstate parameters under different conditions. Our results showed the regulatory effects of PC6 on microstate classes C and D; the occurrence of class C was related to twirling frequency, and the duration of class D was related to acupuncture manipulation. Taken together, our findings indicate that the temporal characteristics of the microstates of classes C and D maybe essential for PC6 acupuncture with twirling frequency manipulation.

The lifting-based methods have dominated monocular 3D human pose estimation by leveraging detected 2D poses as intermediate representations. The 2D component of the final 3D human pose benefits from the detected 2D poses, whereas its depth counterpart must be estimated from scratch. The lifting-based methods encode the detected 2D pose and unknown depth in an entangled feature space, explicitly introducing depth uncertainty to the detected 2D pose, thereby limiting overall estimation accuracy. This work reveals that the depth representation is pivotal for the estimation process. Specifically, when depth is in an initial, completely unknown state, jointly encoding depth features with 2D pose features is detrimental to the estimation process. In contrast, when depth is initially refined to a more dependable state via network-based estimation, encoding it together with 2D pose information is beneficial. To address this limitation, we present a Mixture-of-Experts network for monocular 3D pose estimation named PoseMoE. Our approach introduces: 1) A mixture-of-experts network where specialized expert modules refine the well-detected 2D pose features and learn the depth features. This mixture-of-experts design disentangles the feature encoding process for 2D pose and depth, therefore reducing the explicit influence of uncertain depth features on 2D pose features. 2) A cross-expert knowledge aggregation module is proposed to aggregate cross-expert spatio-temporal contextual information. This step enhances features through bidirectional mapping between 2D pose and depth. Extensive experiments show that our proposed PoseMoE outperforms the conventional lifting-based methods on three widely used datasets: Human3.6M, MPI-INF-3DHP, and 3DPW.

A quick literature survey revealed that many researchers have and continue to work on automatic test pattern generation for analog and mixed-signal circuits and systems, however, very few if any have addressed the problem of test set size. This paper presents a novel test set compaction algorithm which takes a generated test set and maximally reduces the number of test vectors required while maximizing the fault coverage. Results show that 58.33% reduction can be achieved. Smaller test set implies lower total test time and long test times have been identified as one of the bottlenecks in analog and mixed-signal test.

This paper presents an algorithm based on testability measurement for test point insertion of mixed-signal circuits. Two transfer function models compatible with analog models are proposed: one is for digital devices and the other is for A/D interface components. An industry power supply circuit and a common A/D converter circuit are used to validate our approaches.

In this paper, we present an approach to construct a set of dynamic test signals for analog circuits and systems. Testability transfer factors are introduced and we use them as the basis to construct an efficient test set. Fault detectability and fault coverage are defined. Two circuits from the suite of analog and mixed-signal circuits are used to evaluate our approach. The fault coverage is 100% for both circuits studied. The approach presented may be used to construct inputs signals for the selection of an external stimulus applied through an arbitrary waveform generator.

Fast and efficient test generation techniques are key to reducing the current high cost of testing analog circuits. A new multi-frequency test generation technique for detecting catastrophic failures in this class of circuits is presented. Testability transfer factors are introduced and we use them to construct an efficient test set for analog circuits. Fault detectability and fault coverage are also defined. Two circuits from the suite [4] of analog and mixed-signal benchmark circuits are used to validate our approach.